Many individuals and institutions analyze financial data, financial instruments, such as equity and fixed-income securities, and other things, at least in part to predict future economic events. Such individuals may include, for example, security analysts. The role of the security analyst is generally well-known and includes, among other things, issuing earnings estimates for securities, other financial estimates concerning future economic events (e.g., revenue), recommendations on whether investors should buy, sell, or hold financial instruments, such as equity securities, and other predictions. Security analyst estimates may include, but are not limited to, quarterly, semi-annual, and annual earnings estimates for companies whether or not they are traded on a public securities exchange.
Security analysts generally predict a stock's quarterly or annual earnings well in advance of the time the actual earnings are announced, and from time to time, update their predictions. These predictions are recorded, for example, in the Institutional Brokers Estimates Service (“IBES”) database and other commercial databases. The IBES Detail History is complete in its record of estimates and actuals, but limited in its summaries and reports. While IBES provides a summary history database with summary-level information per security per fiscal period (or month), it does not provide daily summaries.
Many investors use the simple average of analysts' estimates, often referred to as the “consensus,” to predict a stock's earnings, and to make investment decisions based on the consensus earnings estimate. However, this consensus is a naïve average created by placing equal weight on each analyst's estimate, regardless of whether the estimate was created recently or months ago, regardless of whether the analyst is a seasoned veteran with a great track record or a rookie, regardless of any historical bias, and regardless of other factors that may be relevant.
Usually more than one analyst follows a given security. Analysts often disagree on earnings estimates and recommendations and, as a result, analysts' earnings estimates and recommendations often vary.
A number of financial information services providers (“FISPs”) gather and report analysts' earnings estimates and recommendations. At least some FISPs report the high, low, and mean (or consensus) earnings estimates, as well as mean recommendations for equity securities (as translated to a FISP's particular scale, for example, one to five). In addition, FISPs may also provide information on what the earnings estimates and recommendations were seven and thirty days prior to the most current consensus, as well as the differences between the consensus (e.g., consensus growth or consensus P/E) for a single equity security and that of the relevant industry. Moreover, for some clients, FISPs provide earnings estimates and recommendations on an analyst-by-analyst basis. An advantage of the availability of analyst-level estimates and recommendations is that a client may view the components of the mean estimate or recommendation by analyst. Various drawbacks exist, however, with these approaches and other known techniques.
For example, prior approaches include a software program that displays all current estimates. For a particular fiscal period, for a particular security, the software provides the ability to simply “include” or “exclude” each estimate or recommendation from the mean. This is problematic for several reasons. First, commercially available databases of estimates and recommendations contain “current” data on thousands of stocks. Each stock may have estimates from 1 to 70 or more analysts. In addition, each analyst may provide estimates for one or more periods. The data may be updated throughout the day. Manually dealing with this volume of information may be time consuming and tedious.
Another drawback is that with current techniques, if an individual were inclined to determine which estimates (or recommendations) should get more weight, and which estimates should get less or no weight, the large volume of analysts makes it extremely difficult to determine which analysts provide more useful information than others. Current techniques lack sufficient ability to intelligently measure historical analyst performance and beneficially use such measurements.
Another drawback is that while it is possible to imagine various weighting systems or algorithms, it is difficult to effectively implement or test them. Current systems do not provide the ability to effectively devise new estimate (or recommendation) weighting algorithms; nor do they provide the ability to easily test a historical performance.
Another drawback with current techniques is that there are limited tools for easily and effectively analyzing historical estimates and recommendations. While the data is available, oftentimes unique code is written to conduct a specific analysis. Changing the analysis often requires rewriting code.
These and other drawbacks exist with existing systems.